Change Detection Method of High Resolution Remote Sensing Image Based on D-S Evidence Theory Feature Fusion

Zhao, Jixiang; Liu, Shanwei; Wan, Jianhua; Yasir, Muhammad; Li, Huayu

doi:10.1109/access.2020.3047915

Cited by 16 publications

(11 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Detection of changes between two images can be considered as a comparison of similarities between two images [35]. The larger the difference between the survey image and the reference image is, the smaller the SSIM will be.…”

Section: Structural Similarity (Ssim) Indexmentioning

confidence: 99%

Vision-Based Tunnel Lining Health Monitoring via Bi-Temporal Image Comparison and Decision-Level Fusion of Change Maps

Attard

Debono

Valentino

et al. 2021

Sensors

View full text Add to dashboard Cite

Tunnel structural health inspections are predominantly done through periodic visual observations, requiring humans to be physically present on-site, possibly exposing them to hazardous environments. These surveys are subjective (relying on the surveyor experience), time-consuming, and may demand operation shutdown. These issues can be mitigated through accurate automatic monitoring and inspection systems. In this work, we propose a remotely operated machine vision change detection application to improve the structural health monitoring of tunnels. The vision-based sensing system acquires the data from a rig of cameras hosted on a robotic platform that is driven parallel to the tunnel walls. These data are then pre-processed using image processing and deep learning techniques to reduce nuisance changes caused by light variations. Image fusion techniques are then applied to identify the changes occurring in the tunnel structure. Different pixel-based change detection approaches are used to generate temporal change maps. Decision-level fusion methods are then used to combine these change maps to obtain a more reliable detection of the changes that occur between surveys. A quantitative analysis of the results achieved shows that the proposed change detection system achieved a recall value of 81%, a precision value of 93% and an F1-score of 86.7%.

show abstract

Section: Structural Similarity (Ssim) Indexmentioning

confidence: 99%

Vision-Based Tunnel Lining Health Monitoring via Bi-Temporal Image Comparison and Decision-Level Fusion of Change Maps

Attard

Debono

Valentino

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Spectral, texture, structural features, and other changes are widely used in existing studies. Based on spectral characteristics, such as the spectral correlation mapper (SCM) [23], the spectral gradient difference (SGD) [23], the Kullback-Leiber divergence [24], and the neighborhood correlation image (NCI) are used for the change detection of remote sensing images. Based on texture features, for instance, the Markov random field (MRF) texture [25], grey level co-occurrence matrix (GLCM) [26,27], and wavelet based textural features [28] are used for the change detection and object extraction of remote sensing images with high spatial resolution.…”

Section: Introductionmentioning

confidence: 99%

“…However, the detection accuracy depends on the quality of the segmentation results [53], so it is worth pondering how to choose the optimal segmentation scale. Moreover, compared to the cumbersome process of the direct object comparison method and the object classification post-comparison method, the idea of directly combining the segmentation result with the initial detection results, for example, the Dempster-Shafer fusion theory [23,54,55], weighted Dempster-Shafer fusion theory [22], and majority voting fusion [29,56,57] can greatly save time and efficiency. As reported in many references, the effectiveness of providing accurate results is different for different types of CD approaches, and the ensemble idea is considered as a key solution to reach a high CD accuracy.…”

Section: Introductionmentioning

confidence: 99%

Object-Oriented Change Detection Method Based on Spectral–Spatial–Saliency Change Information and Fuzzy Integral Decision Fusion for HR Remote Sensing Images

Ding

Molina

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

Spectral features in remote sensing images are extensively utilized to detect land cover changes. However, detection noise appearing in the changing maps due to the abundant spatial details in the high-resolution images makes it difficult to acquire an accurate interpretation result. In this paper, an object-oriented change detection approach is proposed which integrates spectral–spatial–saliency change information and fuzzy integral decision fusion for high-resolution remote sensing images with the purpose of eliminating the impact of detection noise. First, to reduce the influence of feature uncertainty, spectral feature change is generated by three independent methods, and spatial change information is obtained by spatial feature set construction and the optimal feature selection strategy. Secondly, the saliency change map of bi-temporal images is obtained with the co-saliency detection method to complement the insufficiency of image features. Then, the image objects are acquired by multi-scale segmentation based on the staking images. Finally, different pixel-level image change information and the segmentation result are fused using the fuzzy integral decision theory to determine the object change probability. Three high-resolution remote sensing image datasets and three comparative experiments were carried out to evaluate the performance of the proposed algorithm. Spectral–spatial–saliency change information was found to play a major role in the change detection of high-resolution remote sensing images, and the fuzzy integral decision strategy was found to effectively obtain reliable changed objects to improve the accuracy and robustness of change detection.

show abstract

“…In the field of SAR image processing, change detection is a very important topic. SAR images are an important information resource for change detection when studying disaster relief, agricultural detection, and urban planning, especially when evaluating the damage caused by natural disasters [1][2][3][4]. Because of the interference of scattering echo, speckle noise will inevitably be generated; it has the nature of multiplicative noise, and it seriously affects the interpretation of SAR images [5].…”

Section: Introductionmentioning

confidence: 99%

Change Detection from SAR Images Based on Convolutional Neural Networks Guided by Saliency Enhancement

Jia

2021

Remote Sensing

View full text Add to dashboard Cite

Change detection is an important task in identifying land cover change in different periods. In synthetic aperture radar (SAR) images, the inherent speckle noise leads to false changed points, and this affects the performance of change detection. To improve the accuracy of change detection, a novel automatic SAR image change detection algorithm based on saliency detection and convolutional-wavelet neural networks is proposed. The log-ratio operator is adopted to generate the difference image, and the speckle reducing anisotropic diffusion is used to enhance the original multitemporal SAR images and the difference image. To reduce the influence of speckle noise, the salient area that probably belongs to the changed object is obtained from the difference image. The saliency analysis step can remove small noise regions by thresholding the saliency map, and interest regions can be preserved. Then an enhanced difference image is generated by combing the binarized saliency map and two input images. A hierarchical fuzzy c-means model is applied to the enhanced difference image to classify pixels into the changed, unchanged, and intermediate regions. The convolutional-wavelet neural networks are used to generate the final change map. Experimental results on five SAR data sets indicated the proposed approach provided good performance in change detection compared to state-of-the-art relative techniques, and the values of the metrics computed by the proposed method caused significant improvement.

show abstract

Change Detection Method of High Resolution Remote Sensing Image Based on D-S Evidence Theory Feature Fusion

Cited by 16 publications

References 63 publications

Vision-Based Tunnel Lining Health Monitoring via Bi-Temporal Image Comparison and Decision-Level Fusion of Change Maps

Vision-Based Tunnel Lining Health Monitoring via Bi-Temporal Image Comparison and Decision-Level Fusion of Change Maps

Object-Oriented Change Detection Method Based on Spectral–Spatial–Saliency Change Information and Fuzzy Integral Decision Fusion for HR Remote Sensing Images

Change Detection from SAR Images Based on Convolutional Neural Networks Guided by Saliency Enhancement

Contact Info

Product

Resources

About